Fuel imports (% of merchandise imports)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Albania Albania 8.58 -18.9% 66
Argentina Argentina 6.23 -40.8% 79
Armenia Armenia 6.39 -27.3% 76
Antigua & Barbuda Antigua & Barbuda 0.678 -6.21% 85
Australia Australia 13.3 -9.88% 43
Azerbaijan Azerbaijan 8.78 -23.6% 64
Belgium Belgium 16.3 -4.48% 30
Burkina Faso Burkina Faso 41.1 +3.96% 1
Bulgaria Bulgaria 12.3 +10% 47
Bosnia & Herzegovina Bosnia & Herzegovina 12.2 -7.72% 49
Belize Belize 14.3 -3.18% 39
Bolivia Bolivia 29.2 +10.6% 5
Brazil Brazil 13.2 -14.6% 45
Barbados Barbados 22.7 -8.61% 11
Canada Canada 6.29 -9.05% 77
Switzerland Switzerland 3.25 -17.2% 83
Chile Chile 18.9 -9.85% 22
China China 20.4 -2.9% 18
Cyprus Cyprus 22.4 -0.137% 12
Czechia Czechia 5.45 -10.6% 80
Germany Germany 8.04 -6.86% 69
Denmark Denmark 6.91 -46.8% 73
Dominican Republic Dominican Republic 16.6 -8.83% 27
Ecuador Ecuador 25 +3.65% 8
Egypt Egypt 17.9 +18% 24
Spain Spain 13.4 -8.37% 41
Estonia Estonia 8.94 -16.9% 62
Finland Finland 13.6 -4.15% 40
Fiji Fiji 23 -5.41% 9
United Kingdom United Kingdom 10.2 -14.3% 61
Georgia Georgia 11.2 -0.0658% 54
Greece Greece 26.2 -5.65% 6
Grenada Grenada 16.6 -5.82% 28
Guatemala Guatemala 15.3 -8.23% 34
Guyana Guyana 14.8 -23.8% 38
Hong Kong SAR China Hong Kong SAR China 2.11 -5.17% 84
Croatia Croatia 15.5 -3.7% 33
Hungary Hungary 8.82 -8.41% 63
India India 31.5 -5% 3
Ireland Ireland 6.25 -13.6% 78
Iceland Iceland 11.5 -13% 52
Israel Israel 10.2 -12.1% 60
Italy Italy 11.9 -18.8% 50
Japan Japan 22.7 -12.4% 10
Kyrgyzstan Kyrgyzstan 10.7 +48.1% 57
South Korea South Korea 25.7 -3.78% 7
Sri Lanka Sri Lanka 21.5 -13.4% 14
Lithuania Lithuania 18.1 -8.42% 23
Luxembourg Luxembourg 7.47 +3.89% 72
Latvia Latvia 11 -7.74% 55
Macao SAR China Macao SAR China 3.87 -23.1% 82
Moldova Moldova 17.4 -23.4% 25
Maldives Maldives 20.8 -4.46% 17
Mexico Mexico 4.47 -35.2% 81
North Macedonia North Macedonia 11.8 -9.58% 51
Malta Malta 21.2 +32% 15
Myanmar (Burma) Myanmar (Burma) 36.2 +10.6% 2
Montenegro Montenegro 10.5 +1.07% 58
Mauritius Mauritius 21.6 +5.19% 13
Malaysia Malaysia 16.3 -14.4% 29
Namibia Namibia 15.9 -29.3% 32
Netherlands Netherlands 16.2 -13.7% 31
Norway Norway 6.65 -21.6% 75
New Zealand New Zealand 13.4 -7.69% 42
Pakistan Pakistan 31.5 -5.76% 4
Panama Panama 20 -0.526% 19
Philippines Philippines 15 -5.31% 36
Poland Poland 7.59 -14.7% 70
Portugal Portugal 10.8 -7.29% 56
Paraguay Paraguay 13.3 +1.9% 44
French Polynesia French Polynesia 0.374 +21.3% 86
Romania Romania 8.21 -3.63% 68
El Salvador El Salvador 14.8 -9.18% 37
Suriname Suriname 12.9 +7.61% 46
Slovakia Slovakia 8.23 -5.07% 67
Slovenia Slovenia 6.88 -24% 74
Sweden Sweden 10.3 -7.37% 59
Togo Togo 19.4 +34.9% 21
Thailand Thailand 16.9 -8.25% 26
Trinidad & Tobago Trinidad & Tobago 15.3 -58.1% 35
Turkey Turkey 8.65 +3.96% 65
Ukraine Ukraine 12.2 -24.9% 48
United States United States 7.48 -11.1% 71
Uzbekistan Uzbekistan 11.5 +56.5% 53
South Africa South Africa 19.4 -7.65% 20
Zimbabwe Zimbabwe 21.1 +5% 16 

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'TM.VAL.FUEL.ZS.UN'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'TM.VAL.FUEL.ZS.UN'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))